Introduction: Unmasking Tumor Heterogeneity

Tumors are not uniform structures - they are complex ecosystems of malignant, immune, and stromal cells whose interactions shape disease progression and therapy response (1). Capturing this cellular heterogeneity is central to modern oncology. It drives patient stratification, biomarker discovery, and immune profiling, and informs which translational research programs advance.

Single-cell RNA sequencing (scRNA-seq) has provided researchers with an unprecedented window into this complexity, enabling them to distinguish individual cell populations and uncover disease-specific signals (2). But scaling scRNA-seq across large patient cohorts remains prohibitively expensive and technically demanding. Bulk RNA-seq, in contrast, is widely accessible and cost-efficient - but its averaged signals mask the contribution of specific cell types (3).

This trade-off leaves many oncology teams facing a bottleneck: rich but narrow insights from single-cell experiments, or broad but shallow coverage from bulk sequencing. The missing link is cell type deconvolution - a reproducible way to map single-cell resolution onto bulk-scale datasets.

The Impact: Faster, Reproducible, and Scalable Insights

By deploying Elucidata’s Polly platform, a global biopharma team was able to bridge this gap. The solution reduced time-to-insight by 3X, eliminated more than 100 hours of manual processing, and established a reproducible pipeline that could be extended across oncology indications.

Most importantly, the team gained a sustainable framework that combined single-cell precision with cohort-level scalability, enabling faster patient stratification and more confident hypothesis validation.

As one computational biologist in translational oncology reflected:

“We finally had a clean, scalable way to connect single-cell biology to bulk transcriptomics. It transformed our ability to stratify patients and validate hypotheses quickly.”

The Challenge: Making Two Worlds Speak the Same Language

Existing deconvolution tools fell short. Bulk RNA-seq data lacked the resolution required for meaningful immune profiling, while scRNA-seq could not feasibly be applied across hundreds of samples. Fragmented and non-standardized pipelines made integration inconsistent, and manual workflows slowed projects to a crawl.

The consequence was clear: insights into tumor biology and phenotype-level associations were delayed, limiting the impact of translational oncology research.

The Solution: A Scalable Deconvolution Workflow with Polly

To resolve these hurdles, Elucidata worked with the biopharma team to design a reproducible cell type deconvolution workflow powered by Polly. Cell-type signatures derived from AML scRNA-seq datasets were used as reliable references for deconvoluting bulk RNA-seq data.

The pipeline incorporated rigorous quality control, reference mapping, and cell-type estimation using CIBERSORT (4), followed by clustering to reveal phenotype-level associations. Beyond a one-time analysis, Polly delivered reproducible notebooks, automated reports, and QC documentation, creating a sustainable framework that could scale across oncology indications.

Why Scalable Cell Type Deconvolution Matters

For translational research teams, bridging single-cell precision with bulk RNA-seq scalability is no longer a luxury - it is a necessity. Precision oncology requires reproducible pipelines that can unmask immune signatures, stratify patients, and surface phenotype-level insights at speed.

By standardizing and automating cell type deconvolution, biopharma organizations can accelerate their research pipelines, reduce manual bottlenecks, and move more confidently from data to decision.

Looking to scale your single-cell and bulk RNA-seq analysis? Talk to our team about building reproducible deconvolution workflows.

References

1. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–674.
2. Stubbington MJT, et al. Single-cell transcriptomics to explore the immune system in health and disease. Science. 2017;358(6359):58–63.
3. Conesa A, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13.
4. Newman AM, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–457.

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